Geography Reference
In-Depth Information
b
Fig. 12 Some screenshots of WikiCrimes eXplorer (with labeling emphasized for clarity):
a Choropleth (and dot colour) and dot size =
% of crimes reported as occurring in a vehicle
setting (maximum
% labeled for scale), scatterplot
(x) =
% murder
crime victim type,
scatterplot (y) =
% violence crime type; b Choropleth (and dot colour) =
% lack of policing
reason, dot size =
% thoroughfare crime setting (maximum
% labeled for scale), scatterplot
(x) =
% person crime victim type, scatterplot (y) =
% robbery crime type. c Choropleth (and
dot colour) =
% commercial crime setting, dot size =
% poor lighting reason (maximum
%
labeled for scale), scatterplot (x) =
% property crime victim type, scatterplot (y) =
% theft
crime type; details in text. 2011 NComVA.com
Interestingly, vehicle-based crimes occur mostly in states with low proportionate
rates of violence and murder. There is also a spatially-contiguous band of rela-
tively low vehicular crime in the northern states with the exception of Ceará.
(b) shows a generally positive scatterplot relationship between crime on the
person and robbery. There is also a certain amount of choropleth evidence that lack
of policing may be significant as a crime reason in the southern and eastern states.
(c) shows a generally strong positive relationship on the scatterplot between crime
on the property and theft crime type. Also, the states with lower rates of crime in
commercial settings (colour) and with poor lighting as a reason for the crime (dot
size) tend to have higher proportional theft.
Figure
13
shows some screenshots of the visual analysis on the grid-based
displays. Like the displays in Fig.
12
, the choropleth maps in Fig.
13
adopted a
natural breaks strategy for classification. An initial task was to replicate the sce-
narios in Fig.
12
. The results were largely similar, though with significant dif-
ferences. The positive trend on the murder-violence scatterplot in Fig.
12
ais
replicated but the vehicle choropleth shows a different pattern (compare Fig.
6
c
with Fig.
10
c). The person-robbery scatterplot positive trend (Fig.
12
b) is
strengthened in the equivalent plot in the grid implementation. Again, the lack of
policing choropleth has changed (Figs.
7
c and
11
c). The Fig.
12
c property-theft
scatterplot positive trend remains that way in the grid implementation. However,
there is now no observed relationship between poor lighting, commercial setting
and theft activity as observed in Fig.
12
c. The commercial location choropleth has
changed (Figs.
6
d and
10
d); this and the other choropleth changes is a vivid
illustration of MAUP, with the grid-based choropleth having a more objective,
therefore more reliable basis.
Figure
13
a illustrates a weak positive relationship between easy access and
escape for the criminal as a reason, and a commercial crime setting. Also apparent
is a strong positive relationship linking thefts and properties, apparent here by
linking magnitude of the dot size with intensity of the dot colour value.
Figure
13
b shows another weak positive relationship on the scatterplot, this
time between lack of policing as a reason for a crime, and a vehicle crime setting
linking dot size to dot colour suggests a positive relationship between robbery and
crime on the person. In this case and the theft-property link in Fig.
13
a, the strong
positive relationship is more apparent when plotted against each other, as in
Fig.
12
b, c. The murder crime victim type and violence crime type is another
